An image recognition approach to classification of jewelry stone defects

This article is focused on automatic recognition of jewelery stones quality. An image recognition method is described. Relevant image characteristics are computed, which are then used to classify the stone quality. Classification is performed by an algorithm based on binary decision trees with the decision thresholds adapted from a training dataset. At the end, the time complexity as well as accuracy of the proposed algorithm is compared with more than twenty state-of-the-art machine learning algorithms and the results are discussed.